Immunohistochemical Expression of Neurotrophic Tyrosine Kinase Receptors 1 and 2 in Lung Carcinoma: Potential Discriminators Between Squamous and Nonsquamous Subtypes
Bibliographic record
Abstract
Abstract Context .—The neurotrophic tyrosine kinase receptors NTRK1 and NTRK2 have been implicated in the pathogenesis of lung carcinomas. NTRK receptor expression has been reported in lung carcinomas; however, the clinical utility of immunohistochemical expression of these receptors is unclear. Objective .—To compare the immunohistochemical expression profiles of NTRK1 and NTRK2 in various histologic subtypes of lung carcinomas and correlate with patient outcome. Design .—Six hundred eighty-six unique lung cancer cases (including squamous cell carcinoma, adenocarcinoma, large cell carcinoma, small cell carcinoma, and carcinoid tumor) with clinical outcome data in tissue microarray format were immunohistochemically stained for NTRK1 and NTRK2 using commercially available antibodies, automated immunostaining, and standard protocols. Results .—Expression of both NTRK1 and NTRK2 correlates strongly with squamous histology. NTRK1 and NTRK2 are highly specific markers (1: 92.8%, 2: 96.4%) of squamous lung carcinoma when compared with the other carcinoma subtypes, including adenocarcinoma. Positive NTRK2 staining in squamous carcinoma correlates with improved disease-specific survival ( P < .001) and overall survival ( P = .047). Conclusions .—NTRK1 and NTRK2 are potentially useful immunohistochemical markers that may be particularly helpful in separating squamous cell carcinoma from adenocarcinoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".